{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 10.7 Transformer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "import pandas as pd\n",
    "import torch\n",
    "from torch import nn\n",
    "from d2l import torch as d2l"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 10.7.1 基于位置的前馈网络"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "class PositionWiseFFN(nn.Module):\n",
    "    def __init__(self, ffn_num_input, ffn_num_hiddens, ffn_num_outputs, **kwargs):\n",
    "        super(PositionWiseFFN, self).__init__(**kwargs)\n",
    "        self.dense1 = nn.Linear(ffn_num_input, ffn_num_hiddens)\n",
    "        self.relu = nn.ReLU()\n",
    "        self.dense2 = nn.Linear(ffn_num_hiddens, ffn_num_outputs)\n",
    "        \n",
    "    def forward(self, X):\n",
    "        return self.dense2(self.relu(self.dense1(X)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PositionWiseFFN(\n",
       "  (dense1): Linear(in_features=4, out_features=4, bias=True)\n",
       "  (relu): ReLU()\n",
       "  (dense2): Linear(in_features=4, out_features=8, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ffn = PositionWiseFFN(4, 4, 8)\n",
    "ffn.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[[-0.0888,  0.2053,  0.0166,  0.3993, -0.2468,  0.2665,  0.7389,\n",
       "          -0.4149],\n",
       "         [-0.0888,  0.2053,  0.0166,  0.3993, -0.2468,  0.2665,  0.7389,\n",
       "          -0.4149],\n",
       "         [-0.0888,  0.2053,  0.0166,  0.3993, -0.2468,  0.2665,  0.7389,\n",
       "          -0.4149],\n",
       "         [-0.0888,  0.2053,  0.0166,  0.3993, -0.2468,  0.2665,  0.7389,\n",
       "          -0.4149],\n",
       "         [-0.0888,  0.2053,  0.0166,  0.3993, -0.2468,  0.2665,  0.7389,\n",
       "          -0.4149]],\n",
       "\n",
       "        [[-0.0888,  0.2053,  0.0166,  0.3993, -0.2468,  0.2665,  0.7389,\n",
       "          -0.4149],\n",
       "         [-0.0888,  0.2053,  0.0166,  0.3993, -0.2468,  0.2665,  0.7389,\n",
       "          -0.4149],\n",
       "         [-0.0888,  0.2053,  0.0166,  0.3993, -0.2468,  0.2665,  0.7389,\n",
       "          -0.4149],\n",
       "         [-0.0888,  0.2053,  0.0166,  0.3993, -0.2468,  0.2665,  0.7389,\n",
       "          -0.4149],\n",
       "         [-0.0888,  0.2053,  0.0166,  0.3993, -0.2468,  0.2665,  0.7389,\n",
       "          -0.4149]],\n",
       "\n",
       "        [[-0.0888,  0.2053,  0.0166,  0.3993, -0.2468,  0.2665,  0.7389,\n",
       "          -0.4149],\n",
       "         [-0.0888,  0.2053,  0.0166,  0.3993, -0.2468,  0.2665,  0.7389,\n",
       "          -0.4149],\n",
       "         [-0.0888,  0.2053,  0.0166,  0.3993, -0.2468,  0.2665,  0.7389,\n",
       "          -0.4149],\n",
       "         [-0.0888,  0.2053,  0.0166,  0.3993, -0.2468,  0.2665,  0.7389,\n",
       "          -0.4149],\n",
       "         [-0.0888,  0.2053,  0.0166,  0.3993, -0.2468,  0.2665,  0.7389,\n",
       "          -0.4149]]], grad_fn=<AddBackward0>)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ffn(torch.ones((3, 5, 4)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "layer norm: tensor([[-1.0000,  1.0000],\n",
      "        [-1.0000,  1.0000]], grad_fn=<NativeLayerNormBackward>) \n",
      "batch norm: tensor([[-1.0000, -1.0000],\n",
      "        [ 1.0000,  1.0000]], grad_fn=<NativeBatchNormBackward>)\n"
     ]
    }
   ],
   "source": [
    "ln = nn.LayerNorm(2)\n",
    "bn = nn.BatchNorm1d(2)\n",
    "X = torch.tensor([[1, 2], [2, 3]], dtype=torch.float32)\n",
    "print('layer norm:', ln(X), '\\nbatch norm:', bn(X))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "class AddNorm(nn.Module):\n",
    "    def __init__(self, normalized_shape, dropout, **kwargs):\n",
    "        super(AddNorm, self).__init__(**kwargs)\n",
    "        self.dropout = nn.Dropout(dropout)\n",
    "        self.ln = nn.LayerNorm(normalized_shape)\n",
    "        \n",
    "    def forward(self, X, Y):\n",
    "        return self.ln(self.dropout(Y) + X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AddNorm(\n",
       "  (dropout): Dropout(p=0.5, inplace=False)\n",
       "  (ln): LayerNorm((3, 4), eps=1e-05, elementwise_affine=True)\n",
       ")"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "add_norm = AddNorm([3, 4], 0.5)\n",
    "add_norm.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[[0., 0., 0., 0.],\n",
       "         [0., 0., 0., 0.],\n",
       "         [0., 0., 0., 0.]],\n",
       "\n",
       "        [[0., 0., 0., 0.],\n",
       "         [0., 0., 0., 0.],\n",
       "         [0., 0., 0., 0.]]], grad_fn=<NativeLayerNormBackward>)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "add_norm(torch.ones((2, 3, 4)), torch.ones((2, 3, 4)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 10.7.4 编码器"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "class EncoderBlock(nn.Module):\n",
    "    \"\"\"编码器中第i个块\"\"\"\n",
    "    def __init__(self, key_size, query_size, value_size, num_hiddens,\n",
    "                 norm_shape, ffn_num_input, ffn_num_hiddens, num_heads,\n",
    "                 dropout, use_bias=False, **kwargs):\n",
    "        super(EncoderBlock, self).__init__(**kwargs)\n",
    "        self.attention = d2l.MultiHeadAttention(key_size, query_size, value_size, num_hiddens, \n",
    "                                                num_heads, dropout, use_bias)\n",
    "        self.addnorm1 = AddNorm(norm_shape, dropout)\n",
    "        self.ffn = PositionWiseFFN(ffn_num_input, ffn_num_hiddens, num_hiddens)\n",
    "        self.addnorm2 = AddNorm(norm_shape, dropout)\n",
    "        \n",
    "    def forward(self, X, valid_lens):\n",
    "        Y = self.addnorm1(X, self.attention(X, X, X, valid_lens))\n",
    "        return self.addnorm2(Y, self.ffn(Y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EncoderBlock(\n",
       "  (attention): MultiHeadAttention(\n",
       "    (attention): DotProductAttention(\n",
       "      (dropout): Dropout(p=0.5, inplace=False)\n",
       "    )\n",
       "    (W_q): Linear(in_features=24, out_features=24, bias=False)\n",
       "    (W_k): Linear(in_features=24, out_features=24, bias=False)\n",
       "    (W_v): Linear(in_features=24, out_features=24, bias=False)\n",
       "    (W_o): Linear(in_features=24, out_features=24, bias=False)\n",
       "  )\n",
       "  (addnorm1): AddNorm(\n",
       "    (dropout): Dropout(p=0.5, inplace=False)\n",
       "    (ln): LayerNorm((100, 24), eps=1e-05, elementwise_affine=True)\n",
       "  )\n",
       "  (ffn): PositionWiseFFN(\n",
       "    (dense1): Linear(in_features=24, out_features=48, bias=True)\n",
       "    (relu): ReLU()\n",
       "    (dense2): Linear(in_features=48, out_features=24, bias=True)\n",
       "  )\n",
       "  (addnorm2): AddNorm(\n",
       "    (dropout): Dropout(p=0.5, inplace=False)\n",
       "    (ln): LayerNorm((100, 24), eps=1e-05, elementwise_affine=True)\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = torch.ones((2, 100, 24))\n",
    "valid_lens = torch.tensor([3, 2])\n",
    "encoder_blk = EncoderBlock(24, 24, 24, 24, [100, 24], 24, 48, 8, 0.5)\n",
    "encoder_blk.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([2, 100, 24])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoder_blk(X, valid_lens).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "class TransformerEncoder(d2l.Encoder):\n",
    "    \"\"\"嵌入层、位置编码层、加上num_layers个编码器块\"\"\"\n",
    "    def __init__(self, vocab_size, key_size, query_size, value_size, \n",
    "                 num_hiddens, norm_shape, ffn_num_input, ffn_num_hiddens,\n",
    "                 num_heads, num_layers, dropout, use_bias=False, **kwargs):\n",
    "        super(TransformerEncoder, self).__init__(**kwargs)\n",
    "        self.num_hiddens = num_hiddens;\n",
    "        self.embedding = nn.Embedding(vocab_size, num_hiddens)\n",
    "        self.pos_encoding = d2l.PositionalEncoding(num_hiddens, dropout)\n",
    "        self.blks = nn.Sequential()\n",
    "        for i in range(num_layers):\n",
    "            self.blks.add_module(\"block\" + str(i),\n",
    "                EncoderBlock(key_size, query_size, value_size, num_hiddens,\n",
    "                            norm_shape, ffn_num_input, ffn_num_hiddens,\n",
    "                            num_heads, dropout, use_bias))\n",
    "            \n",
    "    def forward(self, X, valid_lens, *args):\n",
    "        # 因为位置编码在-1和1之间，因此embedding乘以其维度的平方根进行缩放，然后与位置编码相加\n",
    "        X = self.pos_encoding(self.embedding(X) * math.sqrt(self.num_hiddens))\n",
    "        self.attention_weights = [None] * len(self.blks)\n",
    "        for i, blk in enumerate(self.blks):\n",
    "            X = blk(X, valid_lens)\n",
    "            self.attention_weights[i] = blk.attention.attention.attention_weights\n",
    "        return X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TransformerEncoder(\n",
       "  (embedding): Embedding(200, 24)\n",
       "  (pos_encoding): PositionalEncoding(\n",
       "    (dropout): Dropout(p=0.5, inplace=False)\n",
       "  )\n",
       "  (blks): Sequential(\n",
       "    (block0): EncoderBlock(\n",
       "      (attention): MultiHeadAttention(\n",
       "        (attention): DotProductAttention(\n",
       "          (dropout): Dropout(p=0.5, inplace=False)\n",
       "        )\n",
       "        (W_q): Linear(in_features=24, out_features=24, bias=False)\n",
       "        (W_k): Linear(in_features=24, out_features=24, bias=False)\n",
       "        (W_v): Linear(in_features=24, out_features=24, bias=False)\n",
       "        (W_o): Linear(in_features=24, out_features=24, bias=False)\n",
       "      )\n",
       "      (addnorm1): AddNorm(\n",
       "        (dropout): Dropout(p=0.5, inplace=False)\n",
       "        (ln): LayerNorm((100, 24), eps=1e-05, elementwise_affine=True)\n",
       "      )\n",
       "      (ffn): PositionWiseFFN(\n",
       "        (dense1): Linear(in_features=24, out_features=48, bias=True)\n",
       "        (relu): ReLU()\n",
       "        (dense2): Linear(in_features=48, out_features=24, bias=True)\n",
       "      )\n",
       "      (addnorm2): AddNorm(\n",
       "        (dropout): Dropout(p=0.5, inplace=False)\n",
       "        (ln): LayerNorm((100, 24), eps=1e-05, elementwise_affine=True)\n",
       "      )\n",
       "    )\n",
       "    (block1): EncoderBlock(\n",
       "      (attention): MultiHeadAttention(\n",
       "        (attention): DotProductAttention(\n",
       "          (dropout): Dropout(p=0.5, inplace=False)\n",
       "        )\n",
       "        (W_q): Linear(in_features=24, out_features=24, bias=False)\n",
       "        (W_k): Linear(in_features=24, out_features=24, bias=False)\n",
       "        (W_v): Linear(in_features=24, out_features=24, bias=False)\n",
       "        (W_o): Linear(in_features=24, out_features=24, bias=False)\n",
       "      )\n",
       "      (addnorm1): AddNorm(\n",
       "        (dropout): Dropout(p=0.5, inplace=False)\n",
       "        (ln): LayerNorm((100, 24), eps=1e-05, elementwise_affine=True)\n",
       "      )\n",
       "      (ffn): PositionWiseFFN(\n",
       "        (dense1): Linear(in_features=24, out_features=48, bias=True)\n",
       "        (relu): ReLU()\n",
       "        (dense2): Linear(in_features=48, out_features=24, bias=True)\n",
       "      )\n",
       "      (addnorm2): AddNorm(\n",
       "        (dropout): Dropout(p=0.5, inplace=False)\n",
       "        (ln): LayerNorm((100, 24), eps=1e-05, elementwise_affine=True)\n",
       "      )\n",
       "    )\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoder = TransformerEncoder(\n",
    "    200, 24, 24, 24, 24, [100, 24], 24, 48, 8, 2, 0.5)\n",
    "encoder.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([2, 100, 24])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoder(torch.ones((2, 100), dtype=torch.long), valid_lens).shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 10.7.5 解码器"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "class DecoderBlock(nn.Module):\n",
    "    \"\"\"解码器中第i个块\"\"\"\n",
    "    def __init__(self, key_size, query_size, value_size, num_hiddens,\n",
    "                 norm_shape, ffn_num_input, ffn_num_hiddens, num_heads,\n",
    "                 dropout, i, **kwargs):\n",
    "        super(DecoderBlock, self).__init__(**kwargs)\n",
    "        self.i = i\n",
    "        self.attention1 = d2l.MultiHeadAttention(\n",
    "            key_size, query_size, value_size, num_hiddens, num_heads, dropout)\n",
    "        self.addnorm1 = AddNorm(norm_shape, dropout)\n",
    "        self.attention2 = d2l.MultiHeadAttention(\n",
    "            key_size, query_size, value_size, num_hiddens, num_heads, dropout)\n",
    "        self.addnorm2 = AddNorm(norm_shape, dropout)\n",
    "        self.ffn = PositionWiseFFN(ffn_num_input, ffn_num_hiddens, num_hiddens)\n",
    "        self.addnorm3 = AddNorm(norm_shape, dropout)\n",
    "        \n",
    "    def forward(self, X, state):\n",
    "        enc_outputs, enc_valid_lens = state[0], state[1]\n",
    "        # 训练阶段，输出序列的所有词元都在同一时间处理，因此state[2][self.i]都初始化为None\n",
    "        # 预测阶段，输出序列是通过词元一个接着一个解码，因此state[2][self.i]包含直到当前时间步第i个块解码的输出表示\n",
    "        if state[2][self.i] is None:\n",
    "            key_values = X\n",
    "        else:\n",
    "            key_values = torch.cat((state[2][self.i], X), axis=1)\n",
    "        state[2][self.i] = key_values\n",
    "        \n",
    "        if self.training:\n",
    "            batch_size, num_steps, _ = X.shape\n",
    "            dec_valid_lens = torch.arange(1, num_steps + 1, device=X.device).repeat(\n",
    "                                                                batch_size, 1)\n",
    "        else:\n",
    "            dec_valid_lens = None\n",
    "        \n",
    "        # 自注意力\n",
    "        X2 = self.attention1(X, key_values, key_values, dec_valid_lens)\n",
    "        Y = self.addnorm1(X, X2)\n",
    "        # 编码器-解码器注意力\n",
    "        Y2 = self.attention2(Y, enc_outputs, enc_outputs, enc_valid_lens)\n",
    "        Z = self.addnorm2(Y, Y2)\n",
    "        return self.addnorm3(Z, self.ffn(Z)), state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecoderBlock(\n",
       "  (attention1): MultiHeadAttention(\n",
       "    (attention): DotProductAttention(\n",
       "      (dropout): Dropout(p=0.5, inplace=False)\n",
       "    )\n",
       "    (W_q): Linear(in_features=24, out_features=24, bias=False)\n",
       "    (W_k): Linear(in_features=24, out_features=24, bias=False)\n",
       "    (W_v): Linear(in_features=24, out_features=24, bias=False)\n",
       "    (W_o): Linear(in_features=24, out_features=24, bias=False)\n",
       "  )\n",
       "  (addnorm1): AddNorm(\n",
       "    (dropout): Dropout(p=0.5, inplace=False)\n",
       "    (ln): LayerNorm((100, 24), eps=1e-05, elementwise_affine=True)\n",
       "  )\n",
       "  (attention2): MultiHeadAttention(\n",
       "    (attention): DotProductAttention(\n",
       "      (dropout): Dropout(p=0.5, inplace=False)\n",
       "    )\n",
       "    (W_q): Linear(in_features=24, out_features=24, bias=False)\n",
       "    (W_k): Linear(in_features=24, out_features=24, bias=False)\n",
       "    (W_v): Linear(in_features=24, out_features=24, bias=False)\n",
       "    (W_o): Linear(in_features=24, out_features=24, bias=False)\n",
       "  )\n",
       "  (addnorm2): AddNorm(\n",
       "    (dropout): Dropout(p=0.5, inplace=False)\n",
       "    (ln): LayerNorm((100, 24), eps=1e-05, elementwise_affine=True)\n",
       "  )\n",
       "  (ffn): PositionWiseFFN(\n",
       "    (dense1): Linear(in_features=24, out_features=48, bias=True)\n",
       "    (relu): ReLU()\n",
       "    (dense2): Linear(in_features=48, out_features=24, bias=True)\n",
       "  )\n",
       "  (addnorm3): AddNorm(\n",
       "    (dropout): Dropout(p=0.5, inplace=False)\n",
       "    (ln): LayerNorm((100, 24), eps=1e-05, elementwise_affine=True)\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "decoder_blk = DecoderBlock(24, 24, 24, 24, [100, 24], 24, 48, 8, 0.5, 0)\n",
    "decoder_blk.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[[ 0.7955,  1.2881, -0.2254,  ...,  1.0031, -1.0658, -0.5707],\n",
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       "       grad_fn=<NativeLayerNormBackward>)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = torch.ones((2, 100, 24))\n",
    "state = [encoder_blk(X, valid_lens), valid_lens, [None]]\n",
    "decoder_blk(X, state)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([2, 100, 24])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "decoder_blk(X, state)[0].shape"
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  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "class TransformerDecoder(d2l.AttentionDecoder):\n",
    "    def __init__(self, vocab_size, key_size, query_size, value_size,\n",
    "                 num_hiddens, norm_shape, ffn_num_input, ffn_num_hiddens,\n",
    "                 num_heads, num_layers, dropout, **kwargs):\n",
    "        super(TransformerDecoder, self).__init__(**kwargs)\n",
    "        self.num_hiddens = num_hiddens\n",
    "        self.num_layers = num_layers\n",
    "        self.embedding = nn.Embedding(vocab_size, num_hiddens)\n",
    "        self.pos_encoding = d2l.PositionalEncoding(num_hiddens, dropout)\n",
    "        self.blks = nn.Sequential()\n",
    "        for i in range(num_layers):\n",
    "            self.blks.add_module(\"block\" + str(i),\n",
    "                DecoderBlock(key_size, query_size, value_size, num_hiddens, norm_shape,\n",
    "                             ffn_num_input, ffn_num_hiddens, num_heads, dropout, i))\n",
    "        self.dense = nn.Linear(num_hiddens, vocab_size)\n",
    "        \n",
    "    def init_state(self, enc_outputs, enc_valid_lens, *args):\n",
    "        return [enc_outputs, enc_valid_lens, [None] * self.num_layers]\n",
    "    \n",
    "    def forward(self, X, state):\n",
    "        X = self.pos_encoding(self.embedding(X) * math.sqrt(self.num_hiddens))\n",
    "        self._attention_weights =[[None] * len(self.blks) for _ in range(2)]\n",
    "        for i, blk in enumerate(self.blks):\n",
    "            X, state = blk(X, state)\n",
    "            # 解码器自注意力权重\n",
    "            self._attention_weights[0][i] = blk.attention1.attention.attention_weights\n",
    "            # 编码器-解码器自注意力权重\n",
    "            self._attention_weights[1][i] = blk.attention2.attention.attention_weights\n",
    "        return self.dense(X), state\n",
    "    \n",
    "    @property\n",
    "    def attention_weights(self):\n",
    "        return self._attention_weights"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 10.7.6 训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_hiddens, num_layers, dropout, batch_size, num_steps = 32, 2, 0.1, 64, 10\n",
    "lr, num_epochs, device = 0.005, 200, d2l.try_gpu()\n",
    "ffn_num_input, ffn_num_hiddens, num_heads = 32, 64, 4\n",
    "key_size, query_size, value_size = 32, 32, 32\n",
    "norm_shape = [32]\n",
    "\n",
    "train_iter, src_vocab, tgt_vocab = d2l.load_data_nmt(batch_size, num_steps)\n",
    "\n",
    "encoder = TransformerEncoder(Je suis de retourJe suis de retourJe suis de retour\n",
    "    len(src_vocab), key_size, query_size, value_size, num_hiddens, norm_shape, \n",
    "    ffn_num_input, ffn_num_hiddens, num_heads, num_layers, dropout)\n",
    "decoder = TransformerDecoder(\n",
    "    len(tgt_vocab), key_size, query_size, value_size, num_hiddens, norm_shape,\n",
    "    ffn_num_input, ffn_num_hiddens, num_heads, num_layers, dropout)\n",
    "net = d2l.EncoderDecoder(encoder, decoder)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss 0.032, 7795.1 tokens/sec on cuda:0\n"
     ]
    },
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   "source": [
    "d2l.train_seq2seq(net, train_iter, lr, num_epochs, tgt_vocab, device)"
   ]
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  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "i'm back . => je suis <unk> .,  bleu 0.512\n",
      "hello world . => <unk> .,  bleu 0.000\n",
      "how are you . => <unk> vous .,  bleu 0.000\n"
     ]
    }
   ],
   "source": [
    "#engs = [\"go .\", \"i lost .\", \"he's calm .\", \"i'm home .\"]\n",
    "#fras = [\"va !\", \"j'ai perdu .\", \"il est calme .\", \"je suis chez moi .\"]\n",
    "engs = [\"i'm back .\", \"hello world .\", \"how are you .\"]\n",
    "fras = [\"je suis de retour .\", \"salut tout le monde .\", \"comment vas-tu .\"]\n",
    "for eng, fra in zip(engs, fras):\n",
    "    translation, dec_attention_weight_seq = d2l.predict_seq2seq(net, eng, \n",
    "                                    src_vocab, tgt_vocab, num_steps, device, True)\n",
    "    print(f'{eng} => {translation}, ',\n",
    "          f'bleu {d2l.bleu(translation, fra, k=2):.3f}')"
   ]
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   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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      ]
     },
     "execution_count": 49,
     "metadata": {},
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   ],
   "source": [
    "enc_attention_weights = torch.cat(net.encoder.attention_weights, 0).reshape(\n",
    "    (num_layers, num_heads, -1, num_steps))\n",
    "enc_attention_weights.shape"
   ]
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   "cell_type": "code",
   "execution_count": 50,
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   "source": [
    "d2l.show_heatmaps(enc_attention_weights.cpu(),\n",
    "                 xlabel='Key position', ylabel='Query positions',\n",
    "                 titles=['Head %d' % i for i in range(1, 5)], figsize=(7, 3.5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(torch.Size([2, 4, 4, 10]), torch.Size([2, 4, 4, 10]))"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "dec_attention_weights_2d = [head[0].tolist() \n",
    "                            for step in dec_attention_weight_seq\n",
    "                            for attn in step for blk in attn for head in blk]\n",
    "dec_attention_weights_filled = torch.tensor(\n",
    "    pd.DataFrame(dec_attention_weights_2d).fillna(0.0).values)\n",
    "dec_attention_weights = dec_attention_weights_filled.reshape(\n",
    "    (-1, 2, num_layers, num_heads, num_steps))\n",
    "dec_self_attention_weights, dec_inter_attention_weights = \\\n",
    "    dec_attention_weights.permute(1, 2, 3, 0, 4)\n",
    "dec_self_attention_weights.shape, dec_inter_attention_weights.shape"
   ]
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   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
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    "d2l.show_heatmaps(\n",
    "    dec_self_attention_weights[:, :, :, :len(translation.split()) + 1],\n",
    "    xlabel='Key positions', ylabel='Query positions',\n",
    "    titles=['Head %d' % i for i in range(1, 5)], figsize=(7, 3.5))"
   ]
  },
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   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
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   "source": [
    "d2l.show_heatmaps(\n",
    "    dec_inter_attention_weights, xlabel='Key positions', ylabel='Query positions',\n",
    "    titles=['Head %d' % i for i in range(1, 5)], figsize=(7, 3.5))"
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